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Boosting With Prior for Accurate Classification
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OA
AI
DOI：10.1109/ACCESS.2023.3281685.png)
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摘要
En 中文
Adaptive Boosting (AdaBoost) based meta learning algorithms generate an accurate classifier ensemble using a learning algorithm with only moderate accuracy guarantees. These algorithms have been designed to work in typical supervised learning settings and hence use only labeled training data along with a base learning algorithm to form an ensemble. However, significant knowledge about the solution space might be available along with training data. The accuracy and convergence rate of AdaBoost might be improved using such knowledge. An effective way to incorporate such knowledge into boosting based ensemble learning algorithms is presented in this paper. Using several synthetic and real datasets, empirical evidence is reported to show the effectiveness of proposed method.Significant improvements have been obtained by applying the proposed method for detecting roads in aerial images.
Keyword:
Classification algorithms
Boosting
Training
Training data
Ensemble learning
Mathematical models
Convergence
AdaBoost
ensemble learning
prior/domain knowledge
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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